- Introduction to ReadAgent by Google DeepMind
- Development of ReadAgent, an AI capable of understanding long texts beyond the limits of its language model.
- Utilizes a human-like reading strategy to comprehend complex documents.
- Challenges Faced by Language Models
- Context length limitation: Fixed token processing capacity leading to performance decline.
- Ineffective context usage: Decreased comprehension with increasing text length.
- Features of ReadAgent
- Mimics human reading by forming and using « gist memories » of texts.
- Breaks down texts into smaller « episodes » and generates gist memories for each.
- Looks up relevant episodes when needed for answering questions.
- Performance Enhancements
- Capable of understanding documents « 20 times longer » than its base language model.
- Shows improved performance on long document question answering datasets:
- QuALITY: Accuracy improved from 85.8% to 86.9%.
- NarrativeQA: Rating increased by 13-32% over baselines.
- QMSum: Rating improved from 44.96% to 49.58%.
- Potential Applications
- Legal contract review, scientific literature analysis, customer support, financial report summarization, automated online course creation.
- Indicates the future potential of AI in mastering lengthy real-world documents through human-like reading strategies.
DoRA: Weight-Decomposed Low-Rank Adaptation
- Objective Exploration: Investigates the disparities between full fine-tuning (FT) and LoRA through a novel weight decomposition analysis.
- Innovative Method: Introduces Weight-Decomposed LowRank Adaptation (DoRA), which splits pre-trained weights into magnitude and direction for fine-tuning.
- Strategic Approach: Employs LoRA for directional updates, significantly reducing the number of trainable parameters.
- Enhanced Performance: By adopting DoRA, it improves learning capacity and training stability of LoRA, without extra inference costs.
- Proven Superiority: Demonstrates that DoRA outperforms LoRA in fine-tuning LLAMA, LLaVA, and VL-BART on tasks like commonsense reasoning, visual instruction tuning, and image/video-text understanding.
- https://arxiv.org/abs/2402.09353
Bunkatopics
Bunkatopics is a package designed for Data Cleaning, Topic Modeling Visualization and Frame Analysis. Its primary goal is to assist developers in gaining insights from unstructured data, potentially facilitating data cleaning and optimizing LLMs through fine-tuning processes. Bunkatopics is constructed using well-known libraries like langchain, chroma, and transformers, enabling seamless integration into various environments.
https://github.com/charlesdedampierre/BunkaTopics?tab=readme-ov-file
LORAX
Multi-LoRA inference server that scales to 1000s of fine-tuned LLMs
LoRAX (LoRA eXchange) is a framework that allows users to serve thousands of fine-tuned models on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency.
LiPO: Listwise Preference Optimization through Learning-to-Rank
- Innovative Framework: LiPO revolutionizes language model alignment by approaching it as a listwise ranking challenge.
- Cutting-Edge Techniques: Utilizes advanced LTR algorithms for a more refined optimization process.
- Superior Performance: LiPO-X method surpasses traditional methods in aligning models with human preferences.
Enhanced Learning Efficiency: Offers a more effective learning paradigm from ranked response lists.
- Scalable Solution: Shows promise for scaling up to larger language model policies across various applications
PyOD, a versatile Python library for detecting anomalies in multivariate data.
Whether you’re tackling a small-scale project or large datasets, PyOD offers a range of algorithms to suit your needs.
- For time-series outlier detection, please use TODS.
- For graph outlier detection, please use PyGOD.
- Performance Comparison & Datasets: We have a 45-page, the most comprehensive anomaly detection benchmark paper. The fully open-sourced ADBench compares 30 anomaly detection algorithms on 57 benchmark datasets.
- Learn more about anomaly detection @ Anomaly Detection Resources
- PyOD on Distributed Systems: you could also run PyOD on databricks.
Llm Visualization
A visualization and walkthrough of the LLM algorithm that backs OpenAI’s ChatGPT. Explore the algorithm down to every add & multiply, seeing the whole process in action.
The Story of RLHF
Origins, Motivations, Techniques, and Modern Applications
- AI development has evolved from early language models like BERT and T5 to advanced Large Language Models (LLMs) like GPT-4.
- The shift from supervised learning to RLHF (Reinforcement Learning from Human Feedback) addresses limitations of earlier models.
- RLHF involves collecting human feedback, training a reward model, and using it to fine-tune LLMs for more aligned outputs.
- RLHF enables LLMs to produce higher quality, human-aligned outputs, especially in tasks like summarization.
- Early RLHF research laid the groundwork for advanced AI systems like InstructGPT and ChatGPT, aiming for long-term alignment of AI with human goals.
https://open.substack.com/pub/cameronrwolfe/p/the-story-of-rlhf-origins-motivations
Why use a RAG ?
Increasingly more business are leveraging AI to augment their organizations and large language models (LLMs) are behind what’s powering these incredible opportunities.
However the process of optimizing LLMs with methods like retrieval augmented generation (RAG) can be complex, which is why we’ll be walking you through everything you should consider before you get started.
RLHF: Reinforcement Learning from Human Feedback
In literature discussing why ChatGPT is able to capture so much of our imagination, I often come across two narratives:
- Scale: throwing more data and compute at it.
- UX: moving from a prompt interface to a more natural chat interface.
One narrative that is often glossed over is the incredible technical creativity that went into making models like ChatGPT work. One such cool idea is RLHF (Reinforcement Learning from Human Feedback): incorporating reinforcement learning and human feedback into NLP.
RL has been notoriously difficult to work with, and therefore, mostly confined to gaming and simulated environments like Atari or MuJoCo. Just five years ago, both RL and NLP were progressing pretty much orthogonally – different stacks, different techniques, and different experimentation setups. It’s impressive to see it work in a new domain at a massive scale.
So, how exactly does RLHF work? Why does it work? This post will discuss the answers to those questions.